Spatially indexed tissue biobank with microscopic phenotype-based retrieval system

ABSTRACT

The invention is directed to a preserved serial set of sequential, iterative, and ordered thin sections of a specimen block, and a catalogue of images taken from one or more sub-series of the set of sections and organized so as to create a three-dimensional profile of the microscopic structures of the specimen block. The serial set of sequential, iterative, and ordered thin sections are prepared from the specimen block by cutting and imaging thin sections of that specimen block so that the thin sections remain strictly in the sequential, iterative, order in which they are cut from the specimen block. A spatial index is created from the catalogued three-dimensional images which is searchable by local content-based image retrieval (LCBIR). The LCBIR query is used to locate, within the three-dimensional index, microscopic structural features, thereby making that portion of the specimen available for further analysis and/or for extraction.

FIELD OF THE INVENTION

The invention is in the field of specimen analysis by histochemistry anddigital imaging techniques.

BACKGROUND

Human tissue samples obtained during surgical procedures or autopsiesare stored for use in future research studies, together with associatedmetadata. The stored tissue samples are frequently subjected tomolecular studies, such as genome or transcriptome analysis. Thus far,standard practice has been to preserve the tissue sample and then storeit as a block of tissue, in a storage facility loosely referred to as a“biobank”. If a researcher or clinician later wished to examine aparticular phenotypic or genotypic feature within a tissue block, thewhole block of tissue would have to be retrieved from storage, and thetissue sectioned to obtain the region of the tissue block of interest.In many cases this region is only a small portion of the tissue in theblock, so the remainder of the tissue is wasted. Another modality in thepractice of anatomic pathology is to cut a tissue block into sections inorder to perform histochemical analysis prior to storage in the tissuedepository. Each tissue section is placed on a glass slide. Some slidesare analyzed with histochemical stains, but other slides, referred to as“blank slides,” are stored unstained. In order to retrieve a region oftissue of interest, it has heretofore been necessary to retrieve a blankslide and slightly stain and visualize a section of the stored tissue todetermine where the region, or “boundary,” may lie. Unfortunately,staining can degrade the very molecule that would have yielded valuableinformation. Further, this procedure is wasteful, because tissue storedin a block must first be sectioned and some sections discarded.

Thus, it would be an advantage to have a system which stores, and isable to retrieve, a small defined microscopic region of a specimen froma specific physical location within a three-dimensional specimen block,based on known specific micro-structural phenotypic information.

SUMMARY

The systems and processes for phenotype-based retrieval of a portion ofa sample disclosed herein can be employed using a tape transfercryostat-microtome technique for generating a plurality of series ofsections of the tissue sample. In a series of tissue sections, at leastone of the series may remain unprocessed for later use as a “blank.” Theblanks, which can be either transferred to slides or retained on thetape, can be transferred to a tissue repository and stored for lateranalysis by, e.g., genomic, protein, or histological analysis. Theremaining sections can be transferred to slides for histochemistry.

Use of a tape transfer technique, either manual tape transfer or theautomated tape transfer system disclosed in U.S. Patent Application Ser.No. 62/187,114, filed Jun. 30, 2015, which is hereby incorporated byreference, results in sections that are relatively non-distorted. Therelative lack of distortion enables alignment of the two-dimensional x-ycoordinates of contiguous sections. In addition, tracking the order ofthe sections as they are created allows for the sections to be uniquelyidentified and ordered relative to each other so that three-dimensionaldata stacking is made possible, thereby, along with un-distortedsections, permitting three-dimensional spatial referencing of the sample(such as the brain) from which the sample was collected.

As disclosed herein, spatial localization within the sections can beachieved by applying spatial reference markings, a.k.a., fiducial marks,to the specimen block from which the sections will be cut so thatabsolute x-y coordinates can be obtained within each section. Spatiallocalization enables two-dimensional mapping of the sections using thefiducial elements disclosed above to develop a uniform coordinate systemacross the section, thus allowing precise identification of features onsections.

The sections targeted for analysis can be stained with histochemicalstains, digitally imaged, and subjected to image processing or otherdata analytics in order to identify and catalog phenotypes for thesample. This spatial index will permit query and retrieval ofappropriate sections of tissue, or precise spatial locations within suchsections, from which material can be extracted for furtherhistochemical, immunohistochemical, or DNA or RNA analysis.

The spatial index can include the cataloged phenotype and its locationon the subject section on which it was identified. Due to thepre-established ordering of the sections, absolute x-y-z coordinates canbe obtained among the sections. Therefore, a pointer to a location on astored, un-analyzed, un-processed section where the cataloged phenotypeis likely to occur, should the unprocessed section be histochemicallystained, digitally imaged, and analyzed, can be developed and includedin the spatial index.

Spatial indexing permits query and retrieval of appropriate unprocessedsections of tissue, or accurate spatial locations within suchunprocessed sections, that are associated with features identified inanalyzed sections. From the appropriate unprocessed sections of tissue,or accurate spatial locations within such unprocessed sections, soretrieved, material can be extracted for further histochemical,immunohistochemical, or DNA/RNA analysis.

The invention is particularly useful in the field of clinical anatomicpathology, where the purpose of retrieving tissue is to obtain furthermolecular information about the same patient from whom the storedsamples were derived, presumably for the diagnostic or therapeuticbenefit of that patient. The invention can also be useful in comparing asecond tissue sample obtained from a patient to a first sample collectedfrom the same patient at an earlier date.

The invention has additional advantages for other patients besides thepatient from whom the stored specimen was obtained. In a first example,a microscopic portion of stored tissue having a specific microscopicphenotype is retrieved from a biobank for the benefit of a secondpatient, who is a different individual from the patient from whom thestored sample was derived. In this case, if a new patient comes in, anda tissue biopsy is performed, then it may be of interest to locate apast case where a similar microscopic phenotype was presented. Thedisclosed system will permit such retrieval based on a histologicalimage from the new patient. Once the section, and a small region of thesection, has been retrieved for the original patient, then this can besubjected to molecular testing, e.g., to determine whether the samemolecular pathology is present and, if so, to look at the therapy and/orprognosis of the previous patient as a guide for the current patient.

In a second example, the invention can be applied for the purpose ofperforming an analysis of a population of patients who share aparticular microscopic phenotype. To do this, the researcher defines themicroscopic phenotype on one sample, and the invented system is used toretrieve bits of tissue from the best-match identified sections ofspecimens taken from each of a larger population of patients. Theanalysis can be, e.g., a molecular analysis, e.g., genomic, proteomic,or transcriptomic analysis; or e.g., a metabolically active analysis,e.g., an enzymatic analysis or an analysis of cellular metabolism. Inother embodiments, the extracted, retrieved portions of specimen aresubjected to a set of molecular tests and population analysis to ask,e.g., whether a given population has a distinctive molecular signatureor pathology.

As used herein, the terms “micro-structure,” “micro-structuralphenotype,” and “microscopic phenotype” refer to a value based on aquantitative measure derived from one or more histologicalcharacteristics visible in a microscopic image of tissue that has beensubjected to one or more histological stains. Without limitation, suchmicro-structural phenotypes may include the number, morphology, typesand spatial distribution of cells in a particular localized region oftissue, such as within a region of diameter about 100 microns, althoughregions as small as 10 microns or as large as the entire section couldbe of interest. The value of the phenotype can be a quantitative measure(number), a qualitative or subjective property (low/high), or a name fora type of the characteristic (lung cancer cell).

Accordingly, in one aspect the invention features a series of planarsections cut from a solid biological sample, which includes: a) planarsections cut sequentially from a solid biological sample; b) a twodimensional coordinate system co-planar to each of the sections to allowfor spatial localization of features identified on the sections; and c)a third dimensional coordinate system perpendicular to the sections toallow for ordered, sequential sections.

Each of the sections can be mounted on a support. As discussed above, atape is adhered to the face of the specimen blockface, so that when themicrotome knife blade cuts a thin section from the specimen, the thinsection is thereby transferred to a tape. The tape can itself serve asthis support for the purposes of the invention. Alternatively, thesection can be transferred from the tape to another support, as detailedherein.

In one embodiment, the solid biological sample is embedded in anembedding medium, and the two dimensional coordinate system is embeddedin the embedding medium surrounding the solid biological sample. As usedherein, “embedding” is the process of sealing a specimen in a supportingmedium in order to keep it intact during cutting of thin sections usinga microtome. The medium can be paraffin wax-based or plastics-based suchas epoxy resins, or mixtures. Embedding mediums are firm media known tothose of skill in the art, such as, e.g., paraffin, wax, polyester wax,paramat, Polyfin™ (Electron Microscopy Sciences, Hatfield, Pa.).

The specimen block can be equipped with fiducial elements from which canbe determined a coordinate system rigidly attached to the specimen. Thefiducial elements can be inserted into and through the entire specimenblock, so that the fiducial elements are integral to the specimen block,and thus integral to any section cut from the specimen block.

In another embodiment, the two dimensional coordinate system is markedon the support.

The series of planar sections cut from a biological sample can furtherinclude a) at least one analyzed section for undergoing featureidentification and location; and b) an unprocessed section for storagewithout undergoing the feature identification and location and havingavailability for future retrieval and analysis.

The series of planar sections cut from the solid biological sample canfurther include: d) a spatial index associated with the sample andincluding a listing of the identified features and a spatiallocalization of the identified features on the ordered, sequentialsections to provide a three dimensional topographical representation forthe sample.

Another aspect of the invention features a set of sections of a solidsample, which includes: a) at least one analyzed section for undergoingfeature identification and location; b) an unprocessed section forstorage without undergoing the feature identification and location andhaving availability for future retrieval and analysis; c) a twodimensional coordinate system on the sections to allow for spatiallocalization of features identified on the at least one analyzedsections; d) a third dimensional coordinate system across the sectionsto allow for ordered, sequential sections; and e) a spatial indexassociated with the sample and including a listing of identifiedfeatures and the spatial localization of the identified features on theordered, sequential sections, wherein the spatial index provides apointer to a location on the unprocessed section in response to a query.Because the sections are in tight registry, in each of the sections ofthe set of sections the two-dimensional coordinate system can besuperimposed on each adjacent section.

In another aspect, the invention features a three-dimensionalrepresentation of a sample, which includes a) digital images of a set ofordered, sequential sections cut from a solid sample, wherein thesections each have a two dimensional coordinate system to provide forspatial localization of features identified on the sections and a thirddimensional coordinate system across the sections to allow for ordered,sequential sections; and b) a spatial index associated with the sample,the spatial index including references to the identified features andthe spatial localization of the identified features on the ordered,sequential sections.

In yet another aspect, the invention features a system for retrievablystoring a set of planar sections cut from a solid specimen, including:a) a sequence of unprocessed planar sections cut from the specimen; b) aspatial index associated with the specimen, the spatial index including(i) reference to a two dimensional coordinate system, the twodimensional coordinate system being co-planar with each of the sectionsto allow for spatial localization of features identified on thesections; (ii) reference to a third dimensional coordinate systemperpendicular to the sections to allow for sequential ordering of thesections; (iii) a listing of identified features on each the section;and (iv) a spatial localization of each the identified feature as afunction of the two dimensional coordinate system and the thirddimensional coordinate system. In some instances, the planar sectionscan be stored on a tape support. The system for retrievably storing aset of planar sections can further include a sequence of processedplanar sections cut from the specimen, wherein the sequence of processedsections is a subsequence of a combination of the sequence ofunprocessed sections and the sequence of processed sections.

In a method of retrievably storing a three-dimensional specimen blockcontaining a microscopic region of interest, the method includes: a)mounting a specimen block on a microtome so as to expose the specimenblockface; b) establishing a two dimensional coordinate system co-planarto each of the sections to allow for spatial localization of featuresidentified on the sections; c) establishing a third coordinate system zperpendicular to the blockface, the third coordinate system z includinga set of ordered, iterative, sequential sections of the specimen, theset of sections prepared by: i) adhering a support to the exposedspecimen blockface; ii) cutting a thin section from the exposed specimenco-planar with the blockface, and removing from the blockface the thinsection attached to the support thereby producing a support-mountedsection having two dimensional (x,y) coordinates co-planar with thesupport and having a support side and a section side; iii) repeatingsteps (i) and (ii) for at least z times; and iv) with each repetition of(i) and (ii) above, retaining the support-mounted sections in sequentialorder of z=1→z=n to preserve a series of support-mounted sections; d)dividing the series of sections into at least two sub-series, andprocessing at least one of the sub-series to identify features of thespecimen, and whereby a second of the at least two sub-series remainsunprocessed; and e) storing the sections in a retrievable storagesystem.

In the method of retrievably storing a three-dimensional specimen blockcontaining a microscopic region of interest, the specimen can beequipped with at least three fiducial elements to establish the twodimensional (x,y) coordinate system co-planar with the blockface.Alternatively, or in addition to, the support is labelled with a set ofcoordinate points to establish the two dimensional (x,y) coordinatesystem co-planar with the blockface. The adhered support can be a tape,or can be another type of support such as a glass or plastic slide. Thesupport is adhered to the section by a chemical adhesive, or by a staticproperty, or by other adhering properties known to those skilled in theart. The support can be labelled with the value n of the thirdcoordinate system z.

In the method of retrievably storing a three-dimensional specimen blockcontaining a microscopic region of interest, the method further includestransferring the section from the tape to a glass slide. Optionally,this can include a) placing the section side of the tape-mounted sectionon a glass slide coated with an ultraviolet-light curable polymer; b)curing the polymer with ultraviolet light; and c) removing the tape fromthe section.

In the method of retrievably storing a three-dimensional specimen blockcontaining a microscopic region of interest, the two dimensionalcoordinates (x,y) of a section z=n are in alignment with the twodimensional coordinates (x,y) of a section z=n+1.

Referring to step d) dividing the series of sections into at least twosub-series, and processing at least one of the sub-series to identifyfeatures of the specimen, the processed sub-series can be treatedhistologically. Processing the sub-series to identify features of thespecimen can further include digital imaging of each section of thesub-series, and also further include analyzing each section of thesub-series to reveal microscopic phenotypic information and cellularfeatures.

A method for feature-based retrieval of a portion of a specimen includesa) developing a catalog of features in the specimen based on analysis ofportions of the specimen according to the method of retrievably storinga three-dimensional specimen block containing a microscopic region ofinterest set forth above; b) developing a spatial index of the catalogedfeatures in the specimen; and c) retrieving the portion of the specimenbased on a selected feature and with reference to the spatial index,wherein the selected feature is associated with a cataloged feature inthe specimen, wherein at least one of the analyzed portions of thespecimen is associated with the cataloged feature, and wherein theretrieved portion of the specimen is different from the at least one ofthe analyzed portions of the specimen that is associated with thecataloged feature.

In method for feature-based retrieval of a portion of a specimen, theretrieved portion of the specimen is proximal to the at least one of theanalyzed portions of the specimen.

In method for feature-based retrieval of a portion of a specimen, theretrieved portion of the specimen further includes retrieving a portionof the specimen that has not undergone analysis but, based on thespatial index, is nearest to the at least one of the analyzed portionsof the specimen that is associated with the cataloged feature.

The method for feature-based retrieval of a portion of a specimen canfurther include: a) cutting the specimen into sections and associatingthe spatial index with the sections in order to develop orderedsections; b) identifying a set of features including the catalogedfeature for a first section in the ordered sections; c) storing thefirst section and information related to the set of features; d)matching the selected feature with the cataloged feature; e) identifyingthe first section with reference to the spatial index and the catalogedfeature; f) identifying a second section proximal to the first sectionbut that has not undergone analysis; and g) retrieving the secondsection. The method for feature-based retrieval of a portion of aspecimen can further include extracting a first aliquot from theretrieved portion of the specimen. This method can also further includea) retrieving a second portion of the specimen with reference to thespatial index, wherein retrieving the second portion is based on anabsence of the selected feature in the second portion; and b) extractinga second aliquot from the second retrieved portion of the specimen tooperate as a baseline in analyzing the first portion.

A method of preparing a series of sections of a specimen includes a)mounting a specimen block on a microtome so as to expose the specimenblockface; b) establishing a two dimensional coordinate system co-planarto each of the sections to allow for spatial localization of featuresidentified on the sections; c) establishing a third coordinate system zperpendicular to the blockface, the coordinate system including a set ofordered, iterative, sequential sections of the specimen, the seriesprepared by: i) adhering a support tape to the specimen blockface; ii)cutting a thin section from the specimen co-planar with the blockface,and removing from the blockface the thin section attached to the supporttape thereby producing a tape-mounted section having two dimensional(x,y) coordinates; iii) repeating steps (i) and (ii) for at least ztimes; and iv) with each repetition of (i) and (ii) above, retaining thetape-mounted sections in sequential order of z=1→z=n to preserve aseries of tape-mounted sections. In the method of preparing a series ofsections of a specimen, the specimen is equipped with at least twofiducial elements to establish the two dimensional (x,y) coordinatesystem co-planar with the blockface. Alternatively, or in addition to,the support can be labelled with a set of coordinate points to establishthe two dimensional (x,y) coordinate system co-planar with theblockface.

The invention further includes a series of planar sections cut from asolid biological sample, including: a) planar sections cut sequentiallyfrom a solid biological sample; b) a two dimensional coordinate systemco-planar to each of the sections to allow for spatial localization offeatures identified on the sections; and c) a third dimensionalcoordinate system perpendicular to the sections to allow for ordered,sequential sections, wherein the series of planar sections is preparedby the method of preparing a series of planar sections described in theparagraph above. The series of planar sections further includes a) atleast one analyzed section for undergoing feature identification andlocation; and b) an unprocessed section for storage without undergoingthe feature identification and location and having availability forfuture retrieval and analysis, also prepared by the method of preparinga series of planar sections described in the paragraph above.

In one aspect is disclosed a series of sections of a sample, includinga) a two dimensional coordinate system on the sections to allow forspatial localization of features identified on sections; b) a thirddimensional coordinate system across the sections to allow for ordered,sequential sections; and c) a spatial index associated with the sampleand comprising a listing of the identified features and the spatiallocalization of the identified features on the ordered, sequentialsections to provide a three dimensional topographical representation ofthe sample. In another embodiment, the series of sections of a samplecan further include a) at least one analyzed section for undergoingfeature identification and location; and b) an unprocessed section forstorage without undergoing the feature identification and location andhaving availability for future retrieval and analysis.

In another aspect is disclosed a set of sections of a sample, whichincludes a) a two dimensional coordinate system on the sections to allowfor spatial localization of features identified on sections; b) a thirddimensional coordinate system across the sections to allow for ordered,sequential sections; and c) a three dimensional topographicalrepresentation of the sample comprising a spatial index associated withthe sample, the spatial index comprising references to the identifiedfeatures and the spatial localization of the identified features on theordered, sequential sections.

In another aspect is disclosed a three-dimensional representation of asample, comprising: a) a set of ordered, sequential sections cut fromthe sample, wherein the sections have a two dimensional coordinatesystem to provide for spatial localization of features identified on thesections; and b) a spatial index associated with the sample, the spatialindex comprising references to the identified features and the spatiallocalization of the identified features on the ordered, sequentialsections.

In another aspect is disclosed a set of sections of a sample,comprising: a) at least one analyzed section for undergoing featureidentification and location; b) an unprocessed section for storagewithout undergoing the feature identification and location and havingavailability for future retrieval and analysis; c) a two dimensionalcoordinate system on the sections to allow for spatial localization offeatures identified on the at least one analyzed sections; d) a thirddimensional coordinate system across the sections to allow for ordered,sequential sections; and e) a spatial index associated with the sampleand comprising a listing of identified features and the spatiallocalization of the identified features on the ordered, sequentialsections, wherein the spatial index provides a pointer to a location onthe unprocessed section where a first identified feature is likely tooccur, in a future analysis of the unprocessed section.

In another aspect is disclosed a set of sections of a sample,comprising: a) at least one analyzed section for undergoing featureidentification and location; b) an unprocessed section for storagewithout undergoing the feature identification and location and havingavailability for future retrieval and analysis; c) a two dimensionalcoordinate system on the sections to allow for spatial localization offeatures identified on the at least one analyzed sections; d) a thirddimensional coordinate system across the sections to allow for ordered,sequential sections; and e) a spatial index associated with the sampleand comprising a listing of identified features and the spatiallocalization of the identified features on the ordered, sequentialsections, wherein the spatial index provides a pointer to a location onthe unprocessed section where a first identified feature is located on aproximal analyzed section.

In yet another aspect is disclosed a method for feature-based retrievalof a portion of a sample, comprising: a) cataloging features in thesample based on analysis of portions of the sample; b) developing aspatial index of the cataloged features in the sample; and c) retrievingthe portion of the sample based on a selected feature and with referenceto the spatial index, wherein the selected feature is associated with acataloged feature in the sample, wherein at least one of the analyzedportions of the sample is associated with the cataloged feature, andwherein the retrieved portion of the sample is different from the atleast one of the analyzed portions of the sample that is associated withthe cataloged feature. In some embodiments of the method, the retrievedportion of the sample is proximal to the at least one of the analyzedportions of the sample. This method can further include retrieving theportion of the sample that has not undergone analysis but, based on thespatial index, is nearest to the at least one of the analyzed portionsof the sample that is associated with the cataloged feature.

In other embodiments of the aforesaid method, the method furtherincludes a) cutting the sample into sections and associating the spatialindex with the sections in order to develop ordered sections; b)identifying a set of features including the cataloged feature for afirst section in the ordered sections; c) storing the first section andinformation related to the set of features; d) matching the selectedfeature with the cataloged feature; e) identifying the first sectionwith reference to the spatial index and the cataloged feature; f)identifying a second section proximal to the first section but that hasnot undergone analysis; and g) retrieving the second section.

Optionally, the method can further include extracting a first aliquotfrom the retrieved portion of the sample. In some cases, the method alsofurther includes a) retrieving a second portion of the sample withreference to the spatial index, wherein retrieving the second portion isbased on an absence of the selected feature in the second portion; andb) extracting a second aliquot from the second retrieved portion of thesample to operate as a baseline in analyzing the first portion.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate one (several) embodiment(s) ofthe invention and together with the description, serve to explain theprinciples of the invention.

FIG. 1A is a perspective side view of one embodiment of the presentinvention, showing fiducial elements embedded into a block of abiological tissue sample;

FIG. 1B is a top view of a section cut from the sample block shown inFIG. 1A;

FIG. 1C is a perspective side view of a second embodiment of the presentinvention, showing a second kind of fiducial elements embedded into ablock of a biological tissue sample;

FIG. 1D is a top view of a section cut from the sample block shown inFIG. 1C;

FIG. 2A is a diagrammatic representation of the sample block of FIG. 1Aand the cutting of series of sections therefrom;

FIG. 2B is a diagrammatic representation of a third embodiment offiducial elements and the development of a coordinate system for usewith identifying positions on sections cut from a block in whichfiducial elements are not embedded;

FIG. 3A is a diagrammatic representation of a process for archivingsections on which features have been identified and mapped, and matchingfeatures on a test sample with features from the archived sections;

FIG. 3B is a diagrammatic representation of another aspect of thecurrent invention, showing the use to which sections retrieved with theprocess of FIG. 3A are put;

FIG. 4A is a block diagram of an exemplary section handling system witha depository system 480 for use in the process of FIG. 3A;

FIG. 4B is a block diagram of one embodiment of the analysis-storagesystem 490 shown in FIG. 4A;

FIG. 5 is a block diagram of one embodiment of the query-retrievalsystem 500 shown in FIG. 4A;

FIG. 6 is a flow chart for one embodiment of an analysis-storage process600; and

FIG. 7 is a flow chart for one embodiment of a query-retrieval process700.

DETAILED DESCRIPTION

The invention is directed to a preserved serial set of sequential,iterative, and ordered thin sections of a specimen block, and acatalogue of images taken from one or more sub-series of the set ofsections and organized so as to create a three-dimensional profile ofthe microscopic structures of the specimen block. The serial set ofsequential, iterative, and ordered thin sections are prepared from thespecimen block by cutting and imaging thin sections of that specimenblock so that the thin sections remain strictly in the sequential,iterative, order in which they are cut from the specimen block. Aspatial index is created from the catalogued three-dimensional imageswhich is searchable by local content-based image retrieval (LCBIR). TheLCBIR query is used to locate, within the three-dimensional index,microscopic structural features, thereby making that portion of thespecimen available for further analysis and/or for extraction. Theprocedure is summarized as follows.

First, one obtains a specimen block which is suitable for sectioning bymicrotome. The specimen block is equipped with fiducial elements fromwhich one can determine a coordinate system rigidly attached to thespecimen. The fiducial elements can be inserted into and through theentire specimen block, or can be attached to the outside of the specimenblock, as described more fully below.

In those situations where the fiducial element is only on the outersurface of the specimen block, the coordinate system defined by thefiducial elements is referred to as “coordinate system A.” The set ofsupports on which the sections are mounted are pre-printed with a secondset of visible fiduciary marks to provide another set of coordinate axesreferred to as “coordinate system B.” When imaging the first series ofsections, the image area should include the fiduciary marks ofcoordinate system B. Since coordinate systems A and B are in rigidregistry, B can substitute for A. In the subsequent steps, coordinatesection B will be used to localize a spatial point on an unprocessedsection from the blank series (the biobanked series of blank tissuesections).

The specimen block (with fiducial elements integral thereto) is cut intoa series of thin sections using a microtome. Sections can be as thin as1 to 5 micrometers (μm), but are more typically in the range of 10, 15,or 20 μm, to 100 μm, or even 200 μm in thickness. Each cut section istransferred to a planar support. The planar support is preferablytransparent, but, depending on the type of staining and imagingemployed, can be translucent or even opaque. In some embodiments, thesupport is a glass slide. Transfer of each cut section to the support isfacilitated using a technique referred to as “tape transfer,” whereby,following each iteration of the microtome blade, a piece of adhesivetape is transferred to the blockface of the specimen block mounted onthe microtome, which is the surface of the specimen block exposed to theknife blade of the microtome. See, e.g., U.S. patent application Ser.No. 62/187,114, filed Jun. 30, 2015, hereby incorporated by reference.

Thus, each newly cut section is released from the specimen blockattached to its own piece of tape. The tape serves as a carrier to ferrythe cut sections from the cutting region (e.g., the knife blade) of themicrotome to a planar support such as a glass microscope slide. Thinsectioning with the tape transfer technique prevents the shape of thethin section from becoming distorted, and thus preserves the precisegeometrical structure from one section to the next. The tape and/orsupport are labelled with a number (z), such that thin section z=1 isthe first thin section cut from the specimen block, and, in the sequence1→n, thin section z=n is the n^(th) section cut from the specimen block.

The series of thin sections are divided into at least two sub-series.The first sub-series of sections is subjected to histological treatment,and subsequently imaged and analyzed to reveal microscopic phenotypicinformation and various cellular features. Additional sub-series can behistologically treated with varying stains and detectable tags. Finally,at least one sub-series of sections, suitably interleaved with thehistologically treated sections, is left unprocessed on the support,either on the tape itself, or on a glass slide support, and stored(a.k.a., “banked”).

The histologically treated sub-series of stained sections, afterimaging, are subjected to image processing algorithms in order to definefeature vectors F(x,y) at each spatial location (x,y) within the planeof the section which is co-planar with the support. A third coordinate,the z coordinate, is defined from the serial number of the section. Tapetransfer assisted sectioning methods make feasible keeping track of thez-coordinate. Thus, at the end one has a feature vector F(x,y,z) foreach point in the three dimensional specimen. In addition, thede-identified patient record number will be called P. Thus, one obtainsa set of feature vectors FP(x,y,z).

These feature vectors are stored in a database, together withinformation which allows precise association with the banked,unprocessed sections. Like the histologically treated sections, theunprocessed sections also have well defined locations (x,y,z) associatedwith them through the coordinate system or systems, with thez-coordinate corresponding to the serial section number, and thede-identified patient record number P. The features are searchable usingLocal-Content-Based-Image-Retrieval (LCBIR).

The next step is a query-retrieval system. In this step, a new sectionof tissue (e.g., from a new patient) is histologically processed, usingthe same or similar histological processing that was used when makingthe samples stored in the biobank. This new section is examined for somefeature associated with a spatial location (e.g., a specific regioncontained within a tumor, such as, e.g., containing specific cell typesas specified by morphology or histochemical stain). This processedsection is imaged, and subjected to the same image processing methodused to create the spatial index, to determine a feature vectorFQ(x′,y′), where Q denotes ‘query’.

The database associated with the specimen bank is then searched bycontent-based searching to obtain a best match between the query featurevector F_(Q)(x′,y′) and a feature vector stored in the database. By wayof example, if the best matched feature vector isFP_(B)(x_(B),y_(B),z_(B)), where P_(B) corresponds to a specific patientrecord, and (x_(B),y_(B),z_(B)) is the best matched physical location inthe banked tissue. This determines a specific thin section, and aspecific spatial location (x_(B),y_(B),z_(B)) on that thin section,which potentially contains a match to the specific location in the newsample. The pair of coordinates (x_(B),y_(B)) denotes the 2D coordinateswithin that specific section, and z_(B) is the z-coordinate associatedwith the same section.

In some cases, where it is desired to extract a small portion of samplefrom the specimen block, a successful retrieval will aid in precisephenotypic selection of a tissue sample that is more accurate thanavailable from patient medical records only, or only from ahistochemical stain on a nearby section but without the ability toprecisely spatially localize the tissue micro-region of interest.

Methods and Materials

Specimens. Specimens which can be analyzed, stored, and retrieved by themethods of the invention include materials that are suitable forlabeling with a fiducial element and cutting into thin sections with theuse of a microtome. Such materials can be either inorganic (e.g.,geological or industrial, such as a part or component with internalstructure that is being subjected to reverse engineering by cutting intothin sections and subsequent imaging), or organic. Organic materials canbe botanical (plant), fungal, or animal materials. In certainembodiments, the specimen is a mammalian tissue, such as those ofanimals commonly used in research, such as rodents (e.g., mice or rats),rabbits, dogs, and primates. Other suitable mammalian tissues are alsothose of animals commonly treated in a veterinary or human clinicalsetting. Specimens can be normal tissue specimens or pathologyspecimens, such as those obtained during clinical trials, during abiopsy, and/or during surgery.

Tissue Preservation. Materials suitable for use in the systems andmethods described herein typically undergo some form of preservation inadvance of sectioning. Where the material is a biological tissue, thetissue is often preserved before sectioning by freezing the tissue as atissue block, or by embedding a block of tissue in paraffin. Optionally,the tissue can be perfusion fixed or immersion fixed tissue.

Preservation of tissue samples can be by freezing or by embedding thetissue in paraffin. Techniques for cryo-protection and freezing oftissue into tissue blocks have been described. See., e.g., Pinskiy etal., “A low-cost technique to cryo-protect and freeze rodent brains,precisely aligned to stereotaxic coordinates for whole-braincryosectioning,” Journal of Neuroscience Methods, vol. 218, pages208-213 (2013). For methods of preparing paraffin-embedded tissueblocks, see Haines, et al., “Technical considerations for developingenzyme immunohistochemical staining procedures on formalin-fixedparaffin-embedded tissues for diagnostic pathology.” J Vet Diagn Invest,1991. 3(1):101-12. Other embedding materials include resins and rubber.Frozen sections can also be embedded (Simonetti et al., J. NeuroscienceMethods, 158:242-250 (2006)).

Fiducial elements. Where specimens are embedded in another material,e.g., paraffin or rubber or another suitable freezing/embedding medium,fiducial elements can be added to the tissue block outside of thespecimen resulting in extrinsic registration without damage to thespecimen. In embodiments in which specimens are not embedded in anothermaterial (the specimen itself being cut into blocks and infiltrated witha hardening substance), fiducial elements can be added to the tissueblock itself, with only minimal damage to the specimen. One method is toplace external fiducial markers by placing three parallel needle tracksin at least three, preferably four, spaced apart locations in theembedding material, and filling the needle tracks with a markersolution, e.g., an acrylamide solution mixed with Indian ink and,optionally, a radioactive component (Simonetti et al., J. NeuroscienceMethods, 158:242-250 (2006); Bussolati et al., “Tissue arrays asfiducial markers for section alignment in 3-D reconstructiontechnology,” J. Cell. Mol. Med., 9(2):438-445 (2005)).

To further avoid distortion or damage to the specimen by the fiducialelement, a fiducial element can be applied solely to the facing surfaceof the specimen block (hereafter referred to as “coordinate system A”).Extrinsic markers can be used either inside or outside the tissuesample. Simonetti et al., J. Neuroscience Meth., 158:242-250 (2006).Registration is extended to sections cut from the interior of thespecimen by a second set of coordinate points (“coordinate system B”)which are printed on the support, i.e., on the slide or tape to whichthe cut section is transferred prior to imaging and digitization.

One exemplary marker solution for forming a fiducial element system in afrozen sample is made of 950 microliters of an 8% acrylamide solutionmixed with 25 microliters of Indian ink and 18 microliters ofiodo[14C]antipyrine solution (25 Ci/ml). Other colorants may also beadded if staining is desired. Three microliters of ammonium persulfate(APS, 30%) and 3 microliters TEMED(N,N,N′,N′-tetramethylethylenediamine) are be added before the markersolution is sucked into a 1 milliliter (ml) syringe equipped with aneedle and a p10 catheter (outer diameter 0.61 mm). Within one minuteafter the APS is added, the syringe is pushed inside each track until itreaches the bottom of the chuck, after which solution is gently pushedout of the syringe. The acrylamide solution is allowed to polymerize for5 minutes, after which the block can be stored at −80° C. Additionalmethods of embedding a fiducial element in specimens and imagingtherefrom are described in, e.g., Simonetti et al., J. NeuroscienceMeth., 158:242-250 (2006).

Each specimen is cut serially into sections of uniform thickness andtransferred to a support by tape transfer. Materials useful as supportsare glass slides, e.g., glass microscope slides; pieces of tape, e.g., apolyimide tape such as Kapton® tape (made from Kapton® HN generalpurpose film available from E.I. du Pont de Nemours and Company(Wilmington, Del.), or CryoJane® tape, made available from LeicaBiosystems of Richmond, Ill.

Suitable materials for use as a support can also include plasticsupports, metal frame mounted polyethylene terephthalate (PET) membraneslides, and metal-framed polyethylene naphthalate (PEN) membrane slides.Golubeva et al., PLOSone June 2013

Tape transfer method. This system relies on the use of an adhesive tapemade, preferably, from a polyimide film, more preferably frompoly-oxydiphenylene-pyromellitimide, which in commercially availableform is called Kapton® (Dupont de Nemours and Company, Wilmington,Del.). The tape is adhered onto the blockface using a roller by eithermanual or mechanical methods. As the block passes across the knife, thetape remains attached to, or adhered to, the surface of the resultingsection so as to prevent the tissue section from deforming. It is anobject of the invention that the shape of the tissue section remain trueto the shape of the original specimen, and thus that the cut tissuesection not be deformed, mutilated, malformed, misshaped,disproportioned, disfigured, truncated, blemished, or marred; nordistorted, twisted, warped, or irregular relative to the parameters ofthe original specimen.

Post-sectioning, the tape/section complex is placed onto a coated glassslide. The slide is pre-coated with a UV polymer; suitable polymers aredescribed in U.S. Pat. No. 5,444,105, “Specimen mounting adhesivecomposition”, issued Aug. 22, 1995, hereby incorporated by reference.

Adhesion between the tissue section and the coated glass slide isachieved by exposure to ultraviolet light (UV). When UV light isapplied, the polymer is cured, resulting in a firm adhesion of thesection onto the slide. The adhesion between section and slide isgreater than that of the adhesion between the section and the adhesivetape, allowing the operator to peel the tape away from the sectionwithout damaging the section. After removal of the adhesive tape, theglass slides can be stained. This process is repeated for all sectionscut from the block. The curing reaction can be performed once per slide,after all sections have been adhered onto the slide. A commercialembodiment of tape transfer based sectioning is available (CryoJane®Tape-Transfer System (developed by Instrumedics, Inc., and now availablefrom Leica Microsystems Inc., Buffalo Grove, Ill.)).

Tape transfer can be achieved manually according to manufacturerinstructions. In certain embodiments, tape transfer is achieved by anautomated method; see, e.g., U.S. Patent Application Ser. No.62/187,114, filed Jun. 30, 2015, hereby incorporated by reference.

Identifiers. Each thin section on a support is indexed with a unique setof identifiers. The first set of identifiers is one or more items ofmetadata. The metadata helps to organize electronic resources, providedigital identification, and support archiving and preservation ofspecimens. Useful types of metadata include date and time of specimencollection, tissue type, sample preparation related data, cross-indexingwith de-identified patient medical records containing diagnostic,therapeutic, prognostic and outcome information, information about otherspecimens or samples from the same patient.

Each section is associated with a numerical value (n) representing thesub-series of thin sections to which it belongs. By way of example, agiven specimen block is cut into a series of three sub-series of thinsections. The first series (n=1) is the first section cut plus eachthird section thereafter. The second series (n=2) is the second sectioncut plus each third section thereafter. The third series (n=3) is thethird section cut plus each third section thereafter.

Each cut section is further associated with a z coordinate value. As thespecimen block is cut by the microtome, the first section cut from themicrotome is assigned a z value of 1, the second cut section is assigneda z value of 2, the third cut section is assigned a z value of 3, and soon. Assuming uniformity of thickness of section cutting, the depth intothe specimen block of any particular thin section, i.e., the distancebetween a thin section and the initial blockface, is the z value timesthe thickness of each cut section. By way of example, when cutting 10micrometer sections, the i^(th) section represents tissue taken z·imicrometers deep into the specimen block.

In certain embodiments, a glass slide or other specimen mount may haveaffixed to it a pre-printed and bar-coded label with the z-coordinatenumber to be assigned to the section to which the tape becomes affixed.It is further preferable that each such label be pre-printed with the nvalue of the sub-series to which the section will be assigned. The labelbar-code can be used for asset tracking, i.e. physically identifying theslide to which the labeled bar-code is attached. The support, e.g.,tape, glass slide, or other support, whether by bar code label ornumerical printing, can have affixed to it any of the above indicated z,n, (x,y) or other coordinate values, metadata identifiers, patientrecord numbers, de-identified patient record numbers, or any otherinformation important to the archiving or retrieval of specificspecimens or portions thereof.

Histology. Each of the sub-series of thin sections which are to beimaged (i.e., all sub-series with the exception of the sub-series of“blank” specimens) are treated to enhance digital imaging of particularfeatures within the specimen. Treatments include histology andhistochemical stains, radiography, or treatment with tagged antibodiesor receptors, or treatment with tagged molecules capable ofcomplementary pairing, e.g., DNA or RNA molecules. By way of example,useful histology stains include hematoxylin and eosin (H&E) stains,uranyl acetate, lead citrate, safranin, oil red O, Congo red, fast greenFCF, toluidine blue, trichrome stains, Wright's stain, Orcein stain, andsilver salts. Histochemical stains include Perls Prussian blue.Radiographic techniques can be used when coupled with steps of X-ray andautoradiography prior to digitization. Staining with tagged moleculescan include, e.g., avidin biotin staining, direct immunoenzyme staining,and indirect immunoenzyme staining. Also useful in the methods of theinvention are florescent tags and colloidal gold. Preferably, specimensare treated with the nissl method, the Golgi method, ion-eriochromecyanine, and luxol fast blue MBS (Kluver H, et al., A method for thecombined staining of cells and fibers in the central nervous system, JNeuropathol Exp Neurol, 1953, 12:400-403; Page, K. M. A stain for myelinusing solochrome cyanin, J Med Lab Technol, 1965, 22:224-225).

With the exception of blank sections, each sub-series of sections istreated with a different histology stain or tag. By way of example, fora specimen block which is cut into three series of thin sections, thefirst series (n=1) can be stained with H&E stain, the second series(n=2) can be treated with nissl stain, and the third series (n=3) can beleft blank.

Imaging and Digitization. Specimens on glass slides are photo-opticallyscanned at high speed to produce high-definition digital images of thespecimen. Imaging can be transmitted (brightfield) and reflected(fluorescence) light microscopy imaging. Preferably, imaging is doneusing a whole-slide microscopic instrument. In one embodiment, digitalimaging can be performed with commercially available equipment. Onenon-limiting example is the NanoZoomer® 2.0 HT (Hamamatsu PhotonicsK.K., Hamamatsu, Japan).

Microscopic phenotypic information and features include spatialdistribution of cells of specific morphologies or exhibiting specificstains; boundary regions of a tumor; and other tissue elements such ascross sections of blood vessels, neuronal processes, or any othercharacteristic microscopic elements of tissue that may undergopathological alteration.

Specimen Depository. Specimens on supports are retained for preservationin storehouses variously referred to as depositories, repositories,banks, biobanks, or tissue banks. Within the depository, the location ofeach support is indexed so that each specific specimen can be retrieved.Preferably, the supports are stored in a storage unit, such as a tray,which is in operable communication with a carrier within an automatedstorage system. When a slide is requested by an operator, the systemdelivers the particular slide to the operator. A non-limiting example ofa system for storing specimens on supports include The AnatomicPathology Slides Storage System (Southwest Solutions Group®, Lewisville,Tex.). Depository services are also provided by Iron Mountain, Inc.(Boston, Mass.). See, e.g., US2013/0319914, “Organizing pathologyassets”, hereby incorporated by reference.

Queries/algorithms for searching. The series of sections which aretreated with stains or tags, after imaging, are subjected to imageprocessing algorithms in order to define feature vectors F(x,y) at eachspatial location (x,y) within the plane of the section. The spatialindex created using the multiple sub-series of sequential, iterative,and ordered thin sections disclosed herein is particularly suited toLocalized Content Based Image Retrieval (LCBIR), which is also known asLocal Query by Image Content (LQBIC) and Local Content-Based VisualInformation Retrieval (LCBVIR). “Content-based” means that the searchanalyzes the contents of the image rather than the metadata (metadatabeing keywords, tags, identifiers or descriptions associated with theimage). The term “content” in this context refers to colors, shapes,textures, or any other information that can be derived from thedigitized stained image, which are indicative of microstructures in thecorresponding segment of the specimen. LCBIR is a CBIR task where theuser is only interested in a portion of the image, not a global orholistic view of the image. Rahmani et al., “Localized Content BasedImage Retrieval”, Abstract, MIR'05, Nov. 10-11, 2005, Singapore; Ma,Ziping, et al., Translation and scale invariants of Legendre moments forImages Retrieval, J. Info. & Comp. sci., 2011, 8(11):2221-2229; Chong,C. W., et al., Translation and scale invariants of Legendre moments,Pattern Recognition, 2004, 37(1):119-129; Teh, C. H. et al., OnImage-Analysis by the Methods of Moments. IEEE Transactions on PatternAnalysis and Machine Intelligence, 1988, 10(4):496-513; Kinoshita, S.K., et al., Content-based retrieval of mammograms using visual featuresrelated to breast density patterns. J Digit Imaging, 2007. 20(2): p.172-90; Shyu et al., Local versus Global Features for Content-BasedImage Retrieval, IEEE Workshop on Content-Based Access of Image andVideo Libraries, 1998.

Retrieval and extraction of features from specimen blanks. As statedabove, a sub-series of sections, interleaved with the processedsections, was left unprocessed. The iterative interleaving of theprocessed and unprocessed series permits each unprocessed section to becharacterized microscopically by its proximal, preferably neighboring,more preferably adjacent, processed sections. After the content-basedquery identifies a thin section indexed according to the best matchedfeature vector, the support with corresponding unstained physical (Useries) section (z_(B)) is then retrieved from storage. A small aliquotof the specimen is extracted from that specimen section (zB),corresponding to the location (x_(B),y_(B)). This step is performed bybringing a suitable extraction device in physical contact with thelocation (x_(B),y_(B)) (e.g., a tissue punch, which can be used on apiece of specimen attached to a tape, or a pipette tip, which can beused to extract a small section of specimen from a support). The tissueextraction device tip is positioned using the coordinate system on thetape or slide, using visual guidance and manual movement, or anequivalent automated procedure using a camera to detect positionaccording to the coordinate system and a stepper motor to position thedevice tip. In another example, the tissue extraction device tip can bepositioned with respect to a coordinate system fixed to the glass slide,which has been registered itself to the tissue in accordance with themethod described in FIG. 2B, described below.

Other methods of extracting a tissue of interest from a blank specimenare known to those of ordinary skill in the art, including laser capturemicrodissection, laser-assisted microdissection, laser induced forwardtransfer, and gravity-assisted microdissection.

The small aliquot of tissue thus extracted is subjected to the desiredmolecular analysis (e.g., DNA or RNA sequencing).

Figures

Reference will now be made in detail to the present exemplaryembodiments, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same or similar reference numbers willbe used throughout the drawings to refer to the same or like parts.Symbols used repeatedly are identified as follows:

-   -   A Coordinate System A for fiducial system 100    -   A′ Alternative embodiment: mapped coordinate system from        coordinate system B    -   B Alternative embodiment: coordinate system for fiducial element        system 210    -   F Feature    -   {F_(CM)} Best match feature candidate set    -   F_(PM) Best match feature    -   F¹ ₁(x₁, y₁, z₁) feature 2 in Section stained with stain 1    -   F¹ ₂(x₂, y₂, z₁) feature 2 in Section stained with stain 2        (ditto re identifiers)    -   F_(Q)(x′, y′) Test feature    -   F_(PM)(x_(PM), y_(PM), z_(PM)) Best match feature vector on a        best match section    -   F^(U) _(PM)(x_(PM), y_(PM), z_(PM)) Best match feature vector on        the unprocessed section associated with the best match section    -   M Best match    -   PM Best match patient    -   P Patient Record number    -   Q Query    -   T test    -   Z₁ 1^(st) position in sample    -   Z_(1→N) 1^(st) through N^(th) position in sample    -   Z_(U) unprocessed position (sometimes Z_(N+1)) in sample

FIGS. 1A and 1B show a sample block 20P prepared for cutting with amicrotome into thin sections. Without limiting the scope of the systemsand processes disclosed here to only biological applications, thematerial contained within the sample block 20P can be a biologicaltissue embedded in paraffin, or the block 20P may itself be a frozenblock of tissue. The letter P is used herein to represent ade-identified patient record number P with which the biological tissuemay be associated.

A fiducial element system 100 can be embedded in the block 20P in orderto provide reference points on sections cut from the block so that acoordinate system can be established and used for mapping positions on asection. See, e.g., Simonetti et al., “A low temperature embedding andsection registration strategy for 3D image reconstruction of the ratbrain from auto-radiographic sections,” Journal of Neuroscience Methods,vol. 158, pages 242-250 (2006). The fiducial elements can be formed asdisclosed above by filling needle tracks in the block with a coloredmarker solution, or by driving rods of a dark-colored and soft materialinto multiple locations in the block. The material can be a plasticcapable of being sectioned.

The fiducial elements system 100 is shown in FIGS. 1A and 1B to havethree fiducial elements 101-1, 101-2, 101-3, in order to provide thethree points from which x and y axes can be established for identifyingpositions in the specimen 27P in a representative section 23P. Morefiducial elements can be used, as will be seen in the four fiducialelements 221-1-221-4, inclusive, shown in FIG. 2b . At least threeplanar fiducial points are required in order to define an x-y coordinatesystem.

FIGS. 1C and 1D show another sample block 40P from the patient P. Asecond fiducial element system 110 with two sheets of material or afolded sheet of material, (capable of being sectioned) and embedded inor adhered to adjacent facing surfaces of the block 40P at right anglesin order to provide x and y axes themselves on a representative section43P from which can be established four identifying positions in thetissue 47P in the section 43P. The material can be a dark-colored andsoft plastic. Alternatively, the fiducial element system 110 can beformed by cutting an angular channel in the block and filling it withthe same colored marker solution.

FIG. 2A is a diagrammatic representation of the sample block 20P fromFIG. 1A and the cutting of series of sections therefrom. When they arecut, a record of the order in which the sections were cut from thesample is made and maintained in order to preserve accurate geometricaland proximity information about the sections relative to each other. Forexample, in the example shown in FIGS. 1A and 1B, sections are cut fromthe sample along an x-y plane, and a unique cutting order identifierassociated with the section can be used to determine where a selectedsection was cut from the specimen, and how far in the direction of the zcoordinate the section was located in the specimen from other sectionsalso identified with a unique cutting order identifier z.

The sections are divided into iterative series in order to conductdifferent forms of analysis. In one example, the analysis can beconducting histochemical analysis on a section, which is subsequentlyimaged, to identify microscopic phenotypic information about the portionof the specimen in that section. For example, the information(hereinafter referred to as “features”) could be the spatialdistribution of cells of specific morphologies or cells exhibitingidentifiable characteristics under specific stains.

Multiple series can be defined in order to subject sections to a varietyof processing procedures. The example shown in FIG. 2A shows a firstseries of sections (Series 1, represented by section 241) to besubjected to a first procedure, such as a first stain, and a secondseries of sections (Series 2, represented by section 242) that issubjected to a second procedure, such as a second stain.

An additional series of sections shown in FIG. 2A (Series U, representedby 24U) is left unprocessed or minimally processed and stored away forfuture use, either on a specimen tape or on a glass slide. The sectionsin Series U are selected based on the order in which they were cut fromthe sample so that, were the sections to be organized into the order inwhich they were cut from the sample, unprocessed sections would beinterleaved with the sections to be processed.

The manner of interleaving can be left to the user. As an example, letZ1 represent the position of the first section cut from the sample. Auser may choose to create two series, with sections assigned to a seriesin the order in which they were cut, Series 1 containing sections withpositions (Z1, Z3, Z5, Z7, . . . ) and Series U containing sections withpositions (Z2, Z4, Z6, Z8, . . . ). Alternatively, the user may chooseto create two series, with fewer sections in Series U than in Series 1.For example, Series U may contain sections with positions (Z4, Z8, Z12,. . . ) and Section 2 may contain the rest.

More complicated series definitions can be developed. For example, auser may choose to define multiple series. When three staining protocolsare desired, a user may choose to assign sections to a series in theorder in which they were cut from the sample, as follows: Series 1:positions (Z1, Z5, Z9, Z13, . . . ); Series 2: positions (Z2, Z6, Z10,Z14, . . . ); Series 3: positions (Z3, Z7, Z11, Z15, . . . ); Series U:positions (Z4, Z8, Z12, Z16, . . . ). A user could also choose toinclude more sections in one series, or to include more frequentsections into Series U. As an example: Series 1: positions (Z1, Z2, Z7,Z8, Z13, Z14, . . . ); Series 2: positions (Z4, Z10, Z16, . . . );Series 3: positions (Z5, Z11, Z17, . . . ); Series U: positions (Z3, Z6,Z12, Z15, Z18. . . ). The unique cutting order identifier for eachsection can be used to organize the sections into series once the seriesare defined. The unique cutting order identifier for a section can becontained in the section's serial number.

Returning to FIG. 2A, when the sections are organized into series fordifferent processing, the fiducial elements 101-1, 101-2, 101-3 providea coordinate system A that allows for a location of features on thesections. In FIG. 2A, the fiducial elements 101-1, 101-2, and 101-3extend through the entire sample and are rigidly attached to each of thesections.

FIG. 2B shows an embodiment of a fiducial element system 210 in whichthe fiducial elements 211-1, 211-2, 211-3 are applied only to the topsurface of the section 60P and do not extend through the length of thesample 60P. When the first section 641 is cut from the sample atposition Z1, it contains fiducial elements 211-1, 211-2, 211-3, but anysubsequent sections (from positions Z2 on) do not. To compensate for thelack of fiducial elements, the sections (641, 642, 643 . . . ) can bemounted on supports (681, 682, 683 . . . ) such as a slide or a sampletape, and fiducial elements 211-1-211-4, marked thereon. An exemplarysample tape is disclosed in U.S. Patent Application 62/187,1147, filedJun. 30, 2015, which has been incorporated by reference.

The coordinate system A′ with which the position of features on thesections (641, 642, 643 . . . ) is determined can be developed bymapping the coordinate system B defined by the fiducial elements 211-1,211-2, 211-3 on section 641 and mounting the sections (641, 642, 643 . .. ) onto the supports (681, 682, 683 . . . ) with great positionalaccuracy so that the sections are all positioned on their supportsexactly the same. The positional accuracy of the mounted sections fromsupport to support renders the coordinate systems A′ and B in rigidregistry so that coordinate system A′ can substitute for coordinatesystem B. In the subsequent steps, coordinate section A′ can be used insubsequent research projects to localize a spatial point on one of theunprocessed sections from series U.

FIG. 3A is a diagrammatic representation of a process for archivingsections on which features have been identified and mapped, and matchingfeatures on a test sample with features from the archived sections.

FIG. 3B is a diagrammatic representation showing the use to whichsections retrieved with the process of FIG. 3A are put. A series ofhistochemical stained sections can be imaged and then subjected to imageprocessing algorithms in order to define features F and feature vectorsF(x,y) at each spatial location (x,y) within a section. A z-coordinatecan be added to the feature vector based on the unique cutting orderidentifier associated with the section bearing the feature. Thus thefeature vector may take the form Fxy(xa, yb, zc), where:

-   -   x identifies the process (such as stain type) to which the        section was subjected;    -   y identifies the feature type,    -   xa identifies the x-coordinate for the feature on the section 24        x;    -   yb identifies the y coordinate for the feature on the section 24        x; and    -   zc identifies the z coordinate of the section 24 x; as        determined by its unique cutting order identifier (which can be        contained in its serial number).

For section 241, which is part of Series 1, a z-coordinate z1 can beidentified from its serial number, which contains the unique cuttingorder identifier that indicates its original location along the z-axisof the sample 20P. Since the tape transfer assisted sectioning method isbeing used, this permits keeping track of the z-coordinate. Thus, thetwo features F1, F2 of section 241 shown in FIG. 3A, have featurevectors F11(x1, y1, z1), F12(x2, y2, z1).

The processed sections, along with information from the analysesperformed on the processed sections, can be stored in the depository480, which contains section storage 485 for storing the processedsections, and one or more databases to store the information developedfrom the analyses. In the embodiment disclosed here, the depository 480may have a features database 488 for storing information about thefeatures identified in the analyses, such as feature types andcalculated feature vectors. The depository 480 may also have an imagedatabase 486 for storing the images made of the processed sections andrelated information about the images and imaging processes. Depository480 also has storage for storing information about the processed andunprocessed sections, such as their serial numbers, including cuttingorder identifiers, the derived z-coordinate corresponding to serialsection number, and the de-identified patient record number, and thecoordinate system used to derive the feature vectors.

In subsequent research projects, a user may search the depository 480 toidentify sections containing features that could be helpful to theirinvestigations. FIG. 3A shows that a new section of tissue, namely testsection 24T from a new patient, can be histochemically processed, usingthe same or similar histochemical processing as those performed bysections stored in the depository 480. The section 24T is examined forsome feature associated with a spatial location (e.g., a specific regioncontained within a tumor, perhaps containing specific cell types asspecified by morphology or histochemical stain). After processing, thesection 24T can be imaged and subjected to the same or similar imageprocessing as sections stored in the depository 480, to determine afeature vector FQ(x′,y′), where Q denotes query.

Depository 480 can be searched to obtain a best match between the queryfeature vector FQ(x′,y′) and a feature vector Fxy(xa, yb, zc) stored indepository 480. The matches are returned as a ranked list based oncloseness to the feature vector in question. To query can be designed toreturn all matches within a set distance of the best match, or return alist of fixed length (e.g., top 10 matches together with the distancescores).

The best match feature vector can be designatedFPM(x_(PM),y_(PM),z_(PM)), where PM corresponds to a specific patientrecord and (x_(PM),y_(PM),z_(PM)) is the physical location in the bankedtissue that best matches the feature under investigation in the newsection 24T. This determines a specific best matched section 24PM, and aspecific spatial location FPM(x_(PM),y_(PM),z_(PM)), on the section24PM, which potentially matches or contains useful information about thefeature under investigation in the new section 24T, or at least providesa starting point for learning more about the feature on section 24T.

Once the best matched section 24PM and feature vectorFPM(x_(PM),y_(PM),z_(PM)) are identified, a series U section 24PMU isidentified that is associated with best matched section 24PM. Inaddition, a best matched feature vector FUPM(x_(PM),y_(PM),z_(PM)),which is the feature vector on the section 24PMU associated with thebest match feature vector FPM(x_(PM),y_(PM),z_(PM)), is identified. Incertain embodiments, the series U section selected to be section 24PMUis the closest in proximity to the best matched section 24PM along thez-axis of the sample from which the sections were cut.

The section 24PMU can be retrieved from the section storage 485, and asmall aliquot of tissue can be obtained from the section 24PMUcorresponding to the location FUPM(x_(PM),y_(PM),z_(PM)), (x_(B),y_(B)).In the embodiment shown in FIG. 3B, a tissue extraction unit 350, havinga tissue extraction device 356, a guidance unit 352 to detect positionaccording to the coordinate system that was used to originally derivethe feature vectors, and a stepper motor 354 to position the tissueextraction device 356. The tissue extraction device 356 can be a tissuepunch, which can be used on a piece of tissue attached to a tape, or apipette, which can be used to extract a small section of tissue.

The tissue extraction device 356 is positioned above the locationFUPM(x_(PM),y_(PM),z_(PM)), (x_(B),y_(B)) using the coordinate systemthat was used to originally derive the feature vectors. The positioningcan be performed manually or automatically using visual guidance fromthe guidance unit 352 and movement by the stepper motor 35. The tissueextraction device 356 is into physical contact with the locationFUPM(x_(PM),y_(PM),z_(PM)), (x_(B),y_(B)), and a small aliquot of tissueobtained. The small aliquot of tissue thus extracted can be subjected tothe desired molecular analysis (eg, DNA or RNA sequencing).

A depository system 400 according to the present invention will now bedescribed in detail with reference to FIGS. 4A-6. The depository system400 is provided for the purposes of analyzing and storing sections cutfrom samples, storing information about the sections and the results ofthe analyses. The depository system 400 is also provided to storeunprocessed sections that can be associated with the analyzed sections,and retrieved for future investigations when the analyzed sections withwhich the unprocessed sections are associated are found to containfeatures that can be helpful in the future investigations.

The depository system 400 has an analysis-storage system 490 that allowsa user to identify features of the sample. As noted above, the featurescould be the spatial distribution of cells of specific morphologies orcells exhibiting identifiable characteristics under specific stains. Thedepository system 400 also allows for storage of information about thefeatures, the analyzed sections, and unprocessed sections that were cutfrom the tissue sample in close proximity to the analyzed sections, foruse in future studies. As seen in FIG. 4A, the depository system 400 canbe electrically and mechanically coupled to a sectioning unit 10 with amicrotome 12 and a tape transport system 14 to provide for controlledcutting of sections from samples. A suitable sectioning unit isdisclosed at U.S. Patent Application 62/187,114, filed Jun. 30, 2015,which has been incorporated by reference.

The depository system 400 also has a query-retrieval system 500 thatallows subsequent users to identify features of a test section, searchfor a best match among earlier identified features, identify theanalyzed section from which the best matched feature was identified, andretrieve an unprocessed section associated with the best matchedidentified section for use by the users. Optionally, the query-retrievalsystem 500 can be electrically and mechanically coupled to a tissueextraction unit 350 to facilitate extraction of an aliquot fromunprocessed sections that have been retrieved from the depository system400.

FIG. 4B is a block diagram of one embodiment of an analysis-storage unit490 of the depository 400. The analysis-storage system 490 providesanalysis of a sample and storage of sections cut therefrom andinformation about the sections and the analysis. As shown in FIG. 4B,the analysis-storage system 490 has an embedding unit 410 for embeddingfiducial elements (such as those shown in FIGS. 1A-1D) into a sample. Asectioning unit 10 has a microtome 12 for cutting the embedded sampleinto sections and a tape transport unit 14 for transporting the samplefrom the embedding unit 410 and the cut sections to the section dividingunit 420 in the analysis-storage system 490.

The analysis-storage system 490 has a section dividing unit 420 formaintaining information about the order in which the sections were cutfrom the sample while dividing the ordered sections into series by thetype of processing to which the sections will be subjected. Theanalysis-storage system 490 also has a Series U section archiving unit430 for preparing the Series U sections for storage, and a cryogenicstorage 482 in a section storage 485 within a depository 480.

For treatment of the sections selected for analysis, theanalysis-storage system 490 has a histochemical analyzer 440 forstaining the sections, an imaging unit 450 for imaging the stainedsections, and an image processor 470 for identifying features of thestained section. In addition, the analysis-storage system 490 also has aSeries 1-n section archiving unit 460 for preparing the analyzedsections for storage, and a temperature and humidity controlled storage484 in the section storage 485 for storing the sections that have beensubjected to processing and analysis.

FIG. 5 is a block diagram of one embodiment of a query-retrieval system500 of the depository 400. The query-retrieval system 500 providesallows users to identify features of a test section, search for a bestmatch among earlier identified features, identify the analyzed sectionfrom which the best matched feature was identified, and retrieve anunprocessed section associated with the best matched identified sectionfor use by the users. As shown in FIG. 5, the query-retrieval system 500has a histochemical analyzer 540 for staining the test sections, animaging unit 550 for imaging the stained test sections, and an imageprocessor 570 for identifying one or more features of the stained testsections.

In addition, the query-retrieval system 500 also has a search engine 580for searching for a best match to the test feature among earlieridentified features, and for identifying the analyzed section from whichthe best matched feature was identified. In certain embodiments,multiple test features can be identified and the search engine 580 mayconduct a search of the feature database 488 to identify best matchesfor each of or a subset of the multiple test features. Thequery-retrieval system 500 may also have a user interface 520 to allow aresearcher to have input into the test feature identification,development of the search query and review of search results.

The search engine 580 can be any suitable type of search engine,including but not limited to a database search engine, a desktop,enterprise or web-based search engine, a centralized or a distributedsearch engine, and search engine. In certain embodiments, the searchengine 580 can be a human-based search engine, in which the researcheror another human filters the search results to clarify their searchrequest and limit the search results The search engine 580 is connectedto the user interface 520 so that a researcher may input searchparameters and receive search results. The search engine 580 may alsohave associated with it a machine learning system 585 to allow thesearch engine 580 to learn from past investigations and past searchesand to improve the accuracy of its search results. The query-retrievalsystem 500 also has a Series U retrieval unit 590 for retrieving anunprocessed section associated with the best matched identified sectionfor use by the users.

In operation, the histochemical analyzer 540, imaging unit 550, andimage processor 570 identify a test feature F_(Q)(x′, y′) for whichsimilar features from past investigations are sought. The test feature(or subset of features) can be user selected from a list of candidatefeatures via a user interface, or selected automatically by the searchengine using the machine learning system 585 and based on the searchingalgorithms used and how informative this feature (or subset of features)has been in the past. Other suitable selection criteria may be used,such as perceived criticality of the feature to investigation. Forsimplicity, the following describes the process 500 for finding a bestmatch for a single test feature F_(Q)(x′, y′). However, it is to beunderstood that the process 500 can be used to identify a best match formultiple test features. The search engine 580 conducts a search of thefeature database 488 to identify a best match feature F_(PM) thatconstitutes a best match to F_(Q)(x′, y′).

A suitable norm or distance in feature space can be utilized todetermine best match. Such a norm could be, for example the Euclideandistance between features vectors, or some other norm such as the p-normbetween Feature vectors. The p-norm between two feature vectors(F1,F2,F3, . . . ) and (F1′, F2′, F3′, . . . ) is defined as[(F1−F1′)^(p)+(F2−F2′)^(p)+(F3−F3′)^(P)+ . . . ]^(1/p), with p=2 givingthe Euclidean norm. Components of the feature vector can be suitablyweighted to emphasize a particular feature that has proven informativein the past. In other embodiments, the search engine 580 may identify aset {F_(CM)} of candidate best match features F_(PM) that couldconstitute a best match to F_(Q)(x′, y′). Returning to the embodiment inwhich the search engine 580 identifies a set {F_(CM)} of candidate bestmatch features for a single test feature, the search engine 580 searchesthe image database to identify the images associated with the members ofthe candidate best match features set {F_(CM)}. In identifying theimages, the search engine 580 determines which sections are associatedwith the candidate best match features.

The search engine 580 determines the best match feature FPM, the featurevector FPM(x_(PM),y_(PM),z_(PM)) that points to the best match featureFPM, and the section 24PM carrying the best match feature FPM and towhich best matched vector FPM(x_(PM),y_(PM),z_(PM)) points. The searchengine may employ artificial intelligence, neural nets, fuzzy logic, andadvanced natural language technology to make its determinations. Inaddition, since the search engine is relatively independent of the othersystems and units described herein, it may use with virtually anyknowledge providing system, such as, for example, a case-based reasoningsystem, to aid in its determination. Retrieval of the spatial index canbe performed using computed metadata (e.g., density of specific types ofcells in space), or by doing content-based image retrieval (i.e.,pattern matching to a query image of interest).

The search engine may access the databases of the depository 480 toobtain information, such as information about the sample, section, theprocesses and procedures underwent by the sections, the types offeatures, the patient, including non-identifying information about thepatient from stored medical records. Further, the search engine mayoutput to the user a set of likely best match features FPM, best matchfeature vectors FPM(x_(PM),y_(PM),z_(PM)), and their associated sections24PM, along with a listing of the extent of match, likelihood ofapplicability, or other ranking for user selection of the most suitablebest match. It is to be understood that no two sections from a singlesample will be precisely the same, as they are cut from different depths(i.e., different z values) in the tissue. However, features in a samplecould be present in more than one section, especially if the sectionsare cut from depths that are close to each other (i.e., geometricallyclose z values) in the tissue. Therefore, multiple sections couldconstitute potential best matches for a test feature. In certainembodiments, the candidate best match features can be returned by thesearch engine 580 as a ranked list (based on closeness to the featurevector in question or based on the extent of similarity of the candidatebest match feature to the test feature). Alternatively, the number ofcandidate best match feature in a set can be limited by criteria such asreturning all matches within a set distance (measured by a set distanceof the best match, or return a list of fixed length (e.g. top 10 matchestogether) with the scores representative of the distance scores, or byscores that represent the extent of similarity of the candidate bestmatch feature to the test feature. One way to identify a best matchfeature from among multiple candidate best match features is to set athreshold value for the match, so that only matches meeting thethreshold (such as an extent of similarity of the candidate best matchfeature to the test feature) are selected. Another way would be toselect one best match using one or more metrics that a best matchfeature would be required to meet.

However the best match is ultimately determined, the search engine 580outputs the best match feature FPM, the feature vectorFPM(x_(PM),y_(PM),z_(PM)), and the section 24PM to the Series U sectionretrieval unit 590, which identifies the unprocessed section 24PMU thatis most closely associated with the section 24PM. In certainembodiments, the section 24PMU is the one that is closest in proximityto the best matched section 24PM along the z-axis of the sample fromwhich the sections were cut. If the unprocessed section that is mostclosely associated to be the unprocessed section 24PMU is unavailable(being damaged, being missing, having already been retrieved in anearlier investigation, or for any other reason), then the unprocessedsection proximate to the unavailable unprocessed section can be selectedas the unprocessed section 24PMU.

The Series U section retrieval unit 590 outputs the identity of thesection 24PMU to the cryogenic storage 482 to retrieve the selectedsection 24PMU. The Series U section retrieval unit 590 also derives thebest match feature vector FUPM(x_(PM),y_(PM),z_(PM)) to identify theposition on the section 24PMU where the best match feature is likely tobe found, based on the feature vector FPM(x_(PM),y_(PM),z_(PM)) of thebest match feature FPM and the close association between best matchsection 24PM and the section 24PMU. The Series U section retrieval unit590 outputs the best match feature vector FUPM(x_(PM),y_(PM),z_(PM)) tothe tissue extraction unit 350 to facilitate extraction of an aliquotfrom the section 24PMU at the location pointed to by the best matchfeature vector FUPM(x_(PM),y_(PM),z_(PM)).

FIG. 6 illustrates an exemplary process 600 for analyzing and storingsections. In one embodiment, a sample block is embedded with fiducialelements to provide a coordinate to facilitate analysis across sectionsin current and future investigation. In a stage 610, the block can becut into sections, with the order in which the sections are cut beingrecorded in a unique cutting order identifier. In a stage 630, sectionsare divided into series, with one or more series targeted forhistochemical analysis and imaging; and one series targeted for no orminimal processing and storage. The sections selected for no or minimalprocessing based on the order in which they were cut from the sample sothat, were the sections to be organized into the order in which theywere cut from the sample, unprocessed sections would be interleaved withthe sections to be processed. In the Stage 630, associations aredeveloped between the sections targeted for processing and thoseselected for Series U sections; store associations. In certainembodiments, an association between a Series U section and a section tobe processed involves an identification of the proximity between the twosections along the z-axis of the sample from which the sections werecut. In an optional stage 640, the Series U sections are mounted on asubstrate for support in preparation for storing in a cryogenic storagein a stage 645.

In a stage 650, the sections targeted for processing are analyzed toidentify microscopic phenotypic information (features). In a stage 660,they are imaged and 3-D locational feature vectors are derived toidentify the location of features identified during the analysis. Thez-axis is determined by reference to the location of the section in thesample before it was cut. In a stage 675, the sections are stored in acontrolled storage environment, while in a stage 685, their associatedimages are stored in an image database, and in a stage 695, the featuresvectors are stored in a feature database.

FIG.7 illustrates an exemplary process 700 for querying the depository480 to identify previously processed sections having test features thatcan be relevant in current research and retrieving unprocessed sectionsstored in the depository 480 that are closely associated with thepreviously processed sections. The progress 700 has a query process 710process with a stage 712 for identifying a query feature FQ from a testsection and developing a query feature vector to pinpoint a location ofthe query vector on the test section.

The process 700 has a stage 714 for identifying a best matched feature(a feature that best matches the query vector) and a best matchedfeature vector that is associated with the best matched feature. In astage 716: the section associated with the best matched feature vector Iidentified, and, in a stage 178, the unprocessed section that had beenstored for subsequent use and that is the most closely associated withthe best match section and its associated best match feature vector isidentified.

The process 700 also has a stage 720 for retrieving the unprocessedsection that is associated with the most closely associated with thebest match section and its associated best match feature vector isidentified.

In a stage 800, a small aliquot of the identified unprocessed section isextracted at a position at the location pointed to by the best matchfeature vector FUPM(x_(PM),y_(PM),z_(PM)).

EXAMPLES

As an illustrative example of the use of the systems and processesdescribed in this patent, it can be desirable to retrieve portions oftissue sections from biopsies performed on a group of patients with aparticular cancer, for example a lung cancer or a breast cancer, with aspecific microscopic phenotype, e.g., an increased number of cells witha given morphological characteristic visible in histological imaging.The retrieved portions of tissue can be subjected to molecular analysesin order to identify mutated genomic sequences, abnormal RNA transcriptsor protein expression. Such analysis may reveal a common genotype ormolecular phenotype corresponding to the microscopic phenotype inquestion, thus leading to better understanding of the pathology, andalso permitting targeted therapy or patient selection in a clinicaltrial.

Such a study could start with creating a series of sections as disclosedabove from a biopsy in a patient. An anatomic pathologist may identify alocal region in one or more histological images, with a staincorresponding to one of the histochemical or immunochemical stainsemployed by the CBIR-assisted biobank disclosed herein. The anatomicpathologist may visually examine the images in question from thepatient's biopsy, and demarcate a small portion of the tissue sectionshowing an abnormal microscopic phenotype (in the judgment of thepathologist), such as the excess in number of cells of a specific type.This histological image may constitute a “query” image, input it to theCBIR-assisted biobank system, along with a demarcation of the smallregion inside the image showing the abnormal microscopic phenotype.

The query mechanism may exploit information present in the query imageat multiple length scales. Part of the weight in the query may be givento the tissue type encompassing the image, and this information ispresent throughout the query image and provides context; similarly, partof the weight will be given to the specific region demarcated within thequery image. In preparing the query, image-processing steps may be takento (i) segment out biologically meaningful objects such as individualcell bodies and/or nuclei present in those cell bodies; (ii) classifyingthose objects, i.e., cell bodies or nuclei, into types; (iii)determining the densities of the corresponding objects of a given type;and (iv) determining the shapes and sizes of regions in which theobjects of specified type are located, from which densities of theobjects may be determined. The rationale for such a procedure is thatpathological tissues may contain objects of abnormal types, or abnormalnumbers or densities of such objects, or abnormal geometricaldistributions of such objects.

Thus, pre-processing of the image may be performed to extract featurescapturing a variety of biologically and histopathologically meaningfulinformation, and the anatomic pathologist may interactively choosebetween the relative weights of such features, in developing the query.It is also possible that the pathologist directly demarcates individualobjects in the image for which the densities are of interest. In thismanner the anatomic pathologist may fine-tune the query beyond selectingan image and demarcating a region of the image, thus improving theprecision of the query based search. The features selected directly orindirectly by the anatomic pathologist are used to formulate the query.In performing the query, the absolute values of these features may beused (such as the densities), in addition the relative values of thefeatures compared with a normative or typical tissue pool may be usedalso to refine the query.

The search inquiry can be developed using any conventional process usingany conventional search engine. As noted above, the test feature (orsubset of features) can be user selected from a list of candidatefeatures via a user interface, or selected automatically by the searchengine using the machine learning system 585 and based on the searchingalgorithms used and extent of usefulness this feature (or subset offeatures) has been in the past. Other suitable selection criteria may beused, such as perceived criticality of the feature to investigation.

Once the region is demarcated the researcher may input just the imageand a demarcation of a region of the image, and allow the search engineto construct the query from the demarcation alone, figuring out why itwas demarcated, such as identifying the characteristic (e.g., an excessof a particular type of cells in the region) based on the regiondemarcation. The demarcation of the region itself can be acharacteristic (location of the region relative to other nearbyregions). The image processor can be used to count the typed cells, thendevelop a density of the identified cells, compare the density to athreshold density, and, if it exceeds the density, look for sectionshaving regions with identified cells of a specified type or density.Alternatively, the search engine and image processor may look forsections having abnormal clusters of the identified cells.

The researcher may input, via the user interface 520, more informationthan just the image and a demarcation of a region of the image. Forexample, the researcher may input information such as why the region wasdemarcated (in this case, an excess of a particular type of cells in theregion). The researcher may input the type of cell, counts of the numberof cells in the demarcated region, the cell density in the region. Theresearcher may indicate what is important to the query—size of region,number of cells, size of cells, type of cells, density, etc.

The system may then proceed in the manner disclosed herein to retrieve asection, and therefore small regions within the section, that providedthe best match to the demarcated abnormal region in the query image. Asa special case, the demarcated region could be the entire section, inwhich case a match will be retrieved for the entire query image. In afurther embodiment, at the direction of the user, multiple sections thatconstitute best matches can be retrieved. As an example, the top 100such matches can be retrieved. In a further embodiment, the searchengine 580 can be configured to limit the multiple best match sectionsto each being associated with different patients or to limit themultiple best match sections to each associated with a different patientsample or section included in the biobank.

Alternatively, the search engine 580 can be configured to allow some orall of the multiple best matched sections to each being associated withthe same patient. The search engine 580 may also be configured to allowonly a certain number of best match sections that may originate from thesame patient. In still further embodiments, the search engine 580 can beconfigured to allow some or all of the multiple best matches sectionsallow a user to select, via the user interface 520, the above-disclosedparameters.

Once the best match sections are identified and retrieved in accordancewith the processes and systems disclosed herein, aliquots of tissue canbe extracted from the identified sections as disclosed herein, and thesecan be subjected to genomic, transcriptomic or proteomic analysis inorder to identify common changes occurring in the abnormal regions onthe test section and the best match section(s).

In another embodiment, when the anatomic pathologist visually examinesthe images in question from the patient's biopsy and demarcates a smallportion of the tissue section showing an abnormal microscopic phenotype,the pathologist may also demarcate a non-pathological or normal portionof the section, to serve as a baseline or comparison. The system maythen proceed in the manner disclosed herein to retrieve the section orsections containing the small regions within those sections that bestmatch the demarcated abnormal region. In so doing, the system alsoretrieves the corresponding “normal” region(s) from the best matchedsection(s). In further embodiments, “normal” regions can be retrievedfrom a different section. Selection of a different section forestablishing a “normal” region baseline may be due to two reasons. Firstsuch a procedure can increase the amount of baseline image material andthis can improve the classification between pathological and normaltissues by better learning the structure of the normal tissues, amethodology that is known as semi-supervised learning. Second, there maynot be enough “normal” or baseline region in the section containing thepathological tissue. The pathologist can select both normal regions witha section, or designate a whole section as “normal”.

The system may then proceed in the manner disclosed herein to extractaliquots of tissue from the identified “abnormal” as well as “normal”regions of the identified “best match” sections. The best match can bedetermined by choosing the closest feature vector corresponding to theregion of interest. A variety of metrics may be used in the space offeature vectors, such as the Euclidean metric, weighted Euclideanmetric, or a p-norm, as will be clear to those skilled in the art ofstatistical analysis. The aliquots of tissue extracted can be used,during genomic, transcriptomic or proteomic analysis, to identify commonchanges occurring in the abnormal regions (as compared to the normalregions). By being able to choose small portions of sections, withmatching microscopic phenotypes, the researcher or physician performingthe study may obtain specific genomic, transcriptomic or proteomicsignatures characteristic of the cancer in question, with high accuracy.The accuracy of the signature would be high because this method choosesa small aliquot of tissue (thus removing confounds due toheterogeneity), and also obtains a matched small aliquot of a normaltissue sample. This kind of study would be uniquely assisted by theCBIR-assisted biobank disclosed herein.

Further, such a procedure may aid a clinical trial by identifyingprevious cases in which a specific microscopic phenotype is present inlocal regions of a cancer biopsy. This could aid patient selection in aclinical trial. For a candidate patient, a biopsy would be performed,and the anatomic pathologist would demarcate abnormal and normal regionswithin stained histological images from this biopsy. Following this,closest matching sections with corresponding microscopic phenotypeswould be identified from the biobank, and aliquots of tissue extracted,as well as the de-identified patient records obtained from the matchingsamples. A criterion for allowing a new patient to be admitted to thestudy in question can be a predetermined level of similarity betweenspecified clinical characteristics of the new patient and those of theprevious best-matched patients in terms of molecular phenotype,diagnostics, therapy and outcome.

It can be seen that the embodiments of the systems and methods disclosedhere can be used to greatly facilitate phenotypic selection of a tissuesample. It will allow for efficient identification of similar featuresand patterns from earlier investigations, and will thus aid in improvingresearch results. Results will be obtained that are more accurate thanavailable from patient medical record only, or only from a histochemicalstain on a nearby section but without the ability to precisely spatiallylocalize the tissue micro-region of interest. When this invention iscombined with other inventions that mechanize tasks such as use of amicrotome itself and subsequent sample manipulation, the entire processcan be automated and placed under computer control.

Thus, presence of a spatial index (constituted by the series of imagesof histologically stained sections, together with processed data forthose sections), will permit accurate and precise retrieval of alocation of interest—not present in current biobanking methods (wherethe entire block or blank section is retrieved). The lack of spatialdistortion due to tape transfer and application of an x-y-z coordinatesystem will permit precise retrieval of an image location, thus savingtissue samples (e.g., only one small element of a biobanked section maythen be retrieved for further analysis).

As shown in FIG. 4A, the depository system 400 has a processor 495electrically coupled to the units and systems disclosed herein forcontrolling the operation of the units and systems. These stepsdisclosed above can be operator controlled using the processor 495inside the depository system 400 (as shown in FIG. 4A), or by acontroller (not shown) outside the system, that communicateselectrically or mechanically with the herein described mechanisms.

The components depicted in the Figures can be operatively connected toone another via a network, such as the Internet or an intranet, or viaany type of wired or wireless communication system. Connections can beimplemented through a direct communication link, a local area network(LAN), a wide area network (WAN) and/or other suitable connections.

One or more of the components depicted in the Figures can be implementedin software on one or more computing systems. For example, they maycomprise one or more applications, which may comprise one or morecomputer-readable instructions which, when executed by a processor,cause a computer to perform steps of a method, or they can be combinedto provide multiple functionalities. Further, while the units andsystems are shown in the Figures as associated with a specificprocessor, such as systems 490, 500 and the processor 495, it is to beunderstood that the units and systems may operate on any other processorshown or not shown.

Further, the instructions for the units and systems can be stored on thestorage device associated with the specific processor or any otherstorage device, or they can be stored on one or more storage devices,and transferred to run on the shown processor or other or multipleprocessors. Computer-readable instructions can be stored on acomputer-readable medium, such as a memory or disk. Such media typicallyprovide non-transitory storage. Alternatively, one or more of thecomponents depicted in FIG. 1 can be hardware components or combinationsof hardware and software such as, for example, special purpose computersor general purpose computers. A computer or computer system may alsocomprise an internal or external database. The components of a computeror computer system may connect through a local bus interface.

One skilled in the art will appreciate that although only one or two ofthe components identified above is depicted in the Figures, any numberof any of these components can be provided. For example, while only onecontroller 495 is shown in FIG. 4A, it is to be understood that multiplecontrollers could be employed instead. Furthermore, one of ordinaryskill in the art will recognize that there can be more than one searchengine 580, or more than one image processor 470.

The databases and storage units shown in FIG. 4B can be implemented asseparate databases and repositories as shown in FIG. 4B or as one ormore internal databases stored, for example, on the processor 495 inFIG. 4A. Units of the depository 480, the image processor 470, and thesearch engine 580 can be accessed by other components in system 400directly via an external connection or via a network (not shown)

What is claimed is:
 1. A series of planar sections cut from a solidbiological sample, comprising: a) planar sections cut sequentially froma solid biological sample; b) a two dimensional coordinate systemco-planar to each of said sections to allow for spatial localization offeatures identified on said sections; and c) a third dimensionalcoordinate system perpendicular to said sections to allow for ordered,sequential sections.
 2. The series of planar sections of claim 1,wherein each of said sections is mounted on a support.
 3. The series ofplanar sections of claim 2, wherein said support is tape.
 4. The seriesof planar sections of claim 2, wherein said solid biological sample isembedded in a medium, and said two dimensional coordinate system isembedded in the medium of said solid biological sample.
 5. The series ofplanar sections of claim 2, wherein said two dimensional coordinatesystem is marked on said support.
 6. The series of claim 1, furthercomprising: a) at least one analyzed section for undergoing featureidentification and location; and b) an unprocessed section for storagewithout undergoing said feature identification and location and havingavailability for future retrieval and analysis.
 7. The series of planarsections cut from the solid biological sample of claim 1, furthercomprising d) a spatial index associated with said sample and comprisinga listing of said identified features and a spatial localization of saididentified features on said ordered, sequential sections to provide athree dimensional topographical representation for said sample.
 8. A setof sections of a solid sample, comprising: a) at least one analyzedsection for undergoing feature identification and location; b) anunprocessed section for storage without undergoing said featureidentification and location and having availability for future retrievaland analysis; c) a two dimensional coordinate system on said sections toallow for spatial localization of features identified on said at leastone analyzed sections; d) a third dimensional coordinate system acrosssaid sections to allow for ordered, sequential sections; and e) aspatial index associated with said sample and comprising a listing ofidentified features and said spatial localization of said identifiedfeatures on said ordered, sequential sections, wherein said spatialindex provides a pointer to a location on said unprocessed section inresponse to a query.
 9. The set of sections of claim 8, wherein saidtwo-dimensional coordinate system can be superimposed on each adjacentsection.
 10. A three-dimensional representation of a sample, comprising:a) digital images of a set of ordered, sequential sections cut from asolid sample, wherein said sections each have a two dimensionalcoordinate system to provide for spatial localization of featuresidentified on said sections and a third dimensional coordinate systemacross said sections to allow for ordered, sequential sections; and b) aspatial index associated with said sample, said spatial index comprisingreferences to said identified features and said spatial localization ofsaid identified features on said ordered, sequential sections.
 11. Asystem for retrievably storing a set of planar sections cut from a solidspecimen, comprising: a) a sequence of unprocessed planar sections cutfrom said specimen; b) a spatial index associated with said specimen,said spatial index comprising (i) reference to a two dimensionalcoordinate system, said two dimensional coordinate system beingco-planar with each of said sections to allow for spatial localizationof features identified on said sections; (ii) reference to a thirddimensional coordinate system perpendicular to said sections to allowfor sequential ordering of said sections; (iii) a listing of identifiedfeatures on each said section; and (iv) a spatial localization of eachsaid identified feature as a function of said two dimensional coordinatesystem and said third dimensional coordinate system.
 12. The system ofclaim 11, wherein said planar sections are stored on a tape support. 13.The system of claim 11, further comprising a sequence of processedplanar sections cut from said specimen, wherein said sequence ofprocessed sections is a subsequence of a combination of said sequence ofunprocessed sections and said sequence of processed sections.
 14. Amethod of retrievably storing a three-dimensional specimen blockcontaining a microscopic region of interest, said method comprising: a)mounting a specimen block on a microtome so as to expose said specimenblockface; b) establishing a two dimensional coordinate system co-planarto each of said sections to allow for spatial localization of featuresidentified on said sections; c) establishing a third coordinate system zperpendicular to said blockface, said third coordinate system zcomprising a set of ordered, iterative, sequential sections of saidspecimen, said set of sections prepared by: i) adhering a support tosaid exposed specimen blockface; ii) cutting a thin section from saidexposed specimen co-planar with said blockface, and removing from saidblockface said thin section attached to said support thereby producing asupport-mounted section having two dimensional (x,y) coordinatesco-planar with said support and having a support side and a sectionside; iii) repeating steps (i) and (ii) for at least z times; and iv)with each repetition of (i) and (ii) above, retaining saidsupport-mounted sections in sequential order of z=1→z=n to preserve aseries of support-mounted sections; d) dividing said series of sectionsinto at least two sub-series, and processing at least one of saidsub-series to identify features of said specimen, and whereby a secondof said at least two sub-series remains unprocessed; and e) storing saidsections in a retrievable storage system.
 15. The method of claim 14,wherein said specimen is equipped with at least three fiducial elementsto establish said two dimensional (x,y) coordinate system co-planar withsaid blockface.
 16. The method of claim 14, wherein said support islabelled with a set of coordinate points to establish said twodimensional (x,y) coordinate system co-planar with said blockface. 18.The method of claim 14, wherein said adhered support is a tape.
 17. Themethod of claim 14, wherein said support is labelled with the value n ofsaid third coordinate system z.
 19. The method of claim 18, wherein saidadherence is by chemical adhesive.
 20. The method of claim 18, whereinsaid adherence is by a static property.
 21. The method of claim 14,wherein said two dimensional coordinates (x,y) of a section z=n are inalignment with said two dimensional coordinates (x,y) of a sectionz=n+1.
 22. The method of claim 18, further comprising transferring saidsection from said tape to a glass slide.
 23. The method of claim 22,comprising: a) placing said section side of said tape-mounted section ona glass slide coated with an ultraviolet-light curable polymer; b)curing said polymer with ultraviolet light; and c) removing said tapefrom said section.
 24. The method of claim 14, wherein processing saidsub-series to identify features of said specimen comprises treating eachsection of said sub-series histologically.
 25. The method of claim 24,wherein processing said sub-series to identify features of said specimenfurther comprises digital imaging of each section of said sub-series.26. The method of claim 25, further comprising analyzing each section ofsaid sub-series to reveal microscopic phenotypic information andcellular features.
 27. A method for feature-based retrieval of a portionof a specimen, comprising: a) developing a catalog of features in saidspecimen based on analysis of portions of said specimen according tosaid method of claim 14 b) developing a spatial index of said catalogedfeatures in said specimen; and c) retrieving said portion of saidspecimen based on a selected feature and with reference to said spatialindex, wherein said selected feature is associated with a catalogedfeature in said specimen, wherein at least one of said analyzed portionsof said specimen is associated with said cataloged feature, and whereinsaid retrieved portion of said specimen is different from said at leastone of said analyzed portions of said specimen that is associated withsaid cataloged feature.
 28. The method of claim 27, wherein saidretrieved portion of said specimen is proximal to said at least one ofsaid analyzed portions of said specimen.
 29. The method of claim 27,wherein retrieving said portion of said specimen further comprisesretrieving a portion of said specimen that has not undergone analysisbut, based on said spatial index, is nearest to said at least one ofsaid analyzed portions of said specimen that is associated with saidcataloged feature.
 30. The method of claim 27, further comprising: a)cutting said specimen into sections and associating said spatial indexwith said sections in order to develop ordered sections; b) identifyinga set of features including said cataloged feature for a first sectionin said ordered sections; c) storing said first section and informationrelated to said set of features; d) matching said selected feature withsaid cataloged feature; e) identifying said first section with referenceto said spatial index and said cataloged feature; f) identifying asecond section proximal to said first section but that has not undergoneanalysis; and g) retrieving said second section.
 31. The method of claim27, further comprising extracting a first aliquot from said retrievedportion of said specimen.
 32. The method of claim 30, furthercomprising: a) retrieving a second portion of said specimen withreference to said spatial index, wherein retrieving said second portionis based on an absence of said selected feature in said second portion;and b) extracting a second aliquot from said second retrieved portion ofsaid specimen to operate as a baseline in analyzing said first portion.33. A method of preparing a series of sections of a specimen, saidmethod comprising: a) mounting a specimen block on a microtome so as toexpose said specimen blockface; b) establishing a two dimensionalcoordinate system co-planar to each of said sections to allow forspatial localization of features identified on said sections; c)establishing a third coordinate system z perpendicular to saidblockface, said coordinate system comprising a set of ordered,iterative, sequential sections of said specimen, said series preparedby: i) adhering a support tape to said specimen blockface; ii) cutting athin section from said specimen co-planar with said blockface, andremoving from said blockface said thin section attached to said supporttape thereby producing a tape-mounted section having two dimensional(x,y) coordinates; iii) repeating steps (i) and (ii) for at least ztimes; and iv) with each repetition of (i) and (ii) above, retainingsaid tape-mounted sections in sequential order of z=1→z=n to preserve aseries of tape-mounted sections.
 34. The method of claim 33, whereinsaid specimen is equipped with at least three fiducial elements toestablish said two dimensional (x,y) coordinate system co-planar withsaid blockface.
 35. The method of claim 33, wherein said support islabelled with a set of coordinate points to establish said twodimensional (x,y) coordinate system co-planar with said blockface.
 36. Aseries of planar sections cut from a solid biological sample,comprising: a) planar sections cut sequentially from a solid biologicalsample; b) a two dimensional coordinate system co-planar to each of saidsections to allow for spatial localization of features identified onsaid sections; and c) a third dimensional coordinate systemperpendicular to said sections to allow for ordered, sequentialsections, wherein said series of planar sections is prepared by themethod of claim
 33. 37. The series of claim 36, further comprising: a)at least one analyzed section for undergoing feature identification andlocation; and b) an unprocessed section for storage without undergoingsaid feature identification and location and having availability forfuture retrieval and analysis, prepared by the method of claim 33.